Talent Groups

Full Stack AI/ML Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Full Stack AI/ML Engineer on a contract basis, requiring 5+ years in data science or ML engineering. Key skills include Python, SQL, MLOps, and cloud platforms. Work is onsite in Irving, TX, with a W2 pay structure.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
November 1, 2025
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Irving, TX
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🧠 - Skills detailed
#PyTorch #Azure #Data Science #AI (Artificial Intelligence) #AWS (Amazon Web Services) #TensorFlow #"ETL (Extract #Transform #Load)" #Storage #GCP (Google Cloud Platform) #NumPy #Model Validation #Deployment #Kubernetes #Pandas #ML (Machine Learning) #Cloud #Langchain #Python #Databases #Docker #SQL (Structured Query Language)
Role description
Talent Groups is leading the search for a Full Stack AI/ML Engineer for an electrical supply company located in Irving, TX. The Engineer will build and deploy AI-driven solutions that address complex business challenges from data to production. The role involves data acquisition, feature engineering, model development, LLM integration, deployment, and ongoing optimization. This is a contract position that will require an onsite working model in Irving, TX. This is a W2 ONLY position. To be immediately and seriously considered for this exceptional career opportunity, you must have the following: • 5+ years of hands-on experience in data science, ML engineering, or applied AI with production deployments. • Strong proficiency in Python (Pandas, NumPy, Scikit, LangChain, and LangGraph, etc.) and SQL. • Experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch. • Skilled in data acquisition, ETL pipelines, and feature engineering using APIs, cloud storage, or databases. • Experience building and managing MLOps pipelines with tools like Docker, Kubernetes, and CI/CD. • Hands-on experience with cloud platforms (Azure, AWS, or GCP) and their ML/AI services. • Working knowledge of Large Language Models (LLMs) and Generative AI frameworks • Strong understanding of EDA, model validation, and experiment tracking. • Familiarity with vector databases for semantic retrieval or RAG pipelines.